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. 2018 Jul 31;8:11489. doi: 10.1038/s41598-018-29768-x

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Tuning of regularization parameters. Normalized mean squared error (NMSE) values are plotted vs. regularization parameters. The NMSE measures the closeness of fit between the deconvolution result and a ground truth data set collected using a confocal microscope scan of the same fluorescent specimen. We evaluated how the choice of regularization parameter effects the NMSE for three deconvolution methods and three different samples with different morphologies. Because the magnitude of regularization parameters is different in different software, the parameters have been independently normalized to the range [0.0, 1.0]. We tested the following regularization parameters: 0.005, 0.010, 0.015, …, 0.100 for our method, 0, 1, 2, …, 20 for Huygens and 15, 105, 205, 305, 405, …, 1005 for Microvolution. Note that the regularization strength increases as the parameter increases in our software but decreases as the parameter increases in Huygens and Microvolution.